On the optimality of Orthogonal Greedy Algorithm for M-coherent dictionaries
Eugene Livshitz
TL;DR
This paper demonstrates that Orthogonal Matching Pursuit achieves near-optimal approximation within a specific number of steps for M-coherent dictionaries, highlighting its effectiveness in sparse approximation tasks.
Contribution
The paper establishes near-optimal performance bounds for Orthogonal Greedy Algorithms on M-coherent dictionaries, extending understanding of their theoretical guarantees.
Findings
Orthogonal Matching Pursuit is nearly optimal for the first 1/(20M) steps.
Provides theoretical bounds for approximation quality.
Highlights effectiveness in sparse approximation with M-coherent dictionaries.
Abstract
We show that Orthogonal Greedy Algorithms (Orthogonal Matching Pursuit) provides almost optimal approximation on the first [1/(20M)] steps for M-coherent dictionaries
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Image and Signal Denoising Methods · Digital Filter Design and Implementation